Privacy sensitive persona management tools

ABSTRACT

The disclosed tools include enhanced and flexible tools to enable users who may be business competitors to share non-generic data in a substantially generic and in a substantially equitable manner. The resulting incentive to more freely share data between competitors will benefit users such as brand owners and enhance content delivered to their end users based on shared data.

This application is a continuation of U.S. application Ser. No.15/146,860 filed May 4, 2016, which is a continuation of U.S. patentapplication Ser. No. 14/280,480 filed May 16, 2014, now U.S. Pat. No.9,348,979, and claims the benefit of and priority to: U.S. ProvisionalPatent Application No. 61/824,353 filed May 16, 2013, each of which isherein incorporated by reference in their entireties.

Related Applications: The following previously filed applications areherein incorporated by reference in their entirety:

U.S. Provisional Patent Application No. 61/493,965;

U.S. Provisional Patent Application No. 61/533,049;

U.S. Provisional Patent Application No. 61/506,601;

U.S. Provisional Patent Application No. 61/567,594;

U.S. Provisional Patent Application No. 61/597,136;

U.S. Provisional Patent Application No. 61/603,216;

U.S. Provisional Patent Application No. 61/683,678;

U.S. Provisional Patent Application No. 61/724,863;

CONSUMER DRIVEN ADVERTISING SYSTEM, U.S. patent application Ser. No.13/490,444 filed Jun. 6, 2012;

SYSTEM AND METHOD FOR DELIVERING ADS TO PERSONAS BASED ON DETERMINEDUSER CHARACTERISTICS, U.S. patent application Ser. No. 13/490,449 filedJun. 6, 2012;

METHOD AND APPARATUS FOR DISPLAYING ADS DIRECTED TO PERSONAS HAVINGASSOCIATED CHARACTERISTICS, U.S. patent application Ser. No. 13/490,447filed Jun. 6, 2012;

CONSUMER DRIVEN ADVERTISING SYSTEM, International Patent Application No.PCT/US12/41178 filed Jun. 6, 2012;

CONSUMER SELF-PROFILING GUI, ANALYSIS AND RAPID INFORMATION PRESENTATIONTOOLS, U.S. application Ser. No. 13/707,581 filed Dec. 6, 2012;

CONSUMER SELF-PROFILING GUI, ANALYSIS AND RAPID INFORMATION PRESENTATIONTOOLS, U.S. application Ser. No. 13/707,578 filed Dec. 6, 2012;

CONSUMER SELF-PROFILING GUI, ANALYSIS AND RAPID INFORMATION PRESENTATIONTOOLS, PCT Application No. PCT/US12/68319 filed Dec. 6, 2012;

AD BLOCKING TOOLS FOR INTEREST-GRAPH DRIVEN PERSONALIZATION, U.S. patentapplication Ser. No. 13/843,635 filed Mar. 15, 2013;

REVERSE BRAND SORTING TOOLS FOR INTEREST-GRAPH DRIVEN PERSONALIZATION,U.S. patent application Ser. No. 13/843,270 filed Mar. 15, 2013;

TOOLS FOR INTEREST GRAPH-DRIVEN PERSONALIZATION, PCT Patent ApplicationNo. PCT/US13/32643 filed Mar. 15, 2013.

The technology described in these applications as well as the currentapplication are interoperable.

APPENDICES

Appendix A has a summary description of the technologies described inthe incorporated applications.

BACKGROUND

Currently, consumer users of email, e-commerce sites and databases aswell as brand owners lack tools to efficiently and conveniently managetheir account information. Specifically, these users lack ability toeasily, equitably and anonymously/generically share their informationand access each other's anonymous/generic information. Sharing dataacross these users may help all of the users supplement their ownproprietary (non-generic) information in order to facilitate betterdelivery of meaningful and personalized content and for other variousmarketing and advertising uses.

What is needed are enhanced and flexible tools to enable users such asthe above to share non-proprietary data in a substantially fair manner.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates an exemplary embodiment comprising: consumer end userdata input, database/interest graph creation, audience engine and brandowner data communication/sharing and finally uses of the aforementionedshared data;

FIG. 2 illustrates an exemplary system of an audience engine and brandowner data communication/sharing and their respective exemplary datapartitions;

FIG. 3 illustrates an exemplary embodiment of partition data representedas interest graphs;

FIG. 4 illustrates an exemplary sub-partition created using partitionsfrom the previous figure;

FIG. 5 illustrates one exemplary tool used to genericize privatepartition data into generic partition data which is subsequently storedwithin a sub-partition;

FIG. 6 illustrates private and sub-partition interchangeability with acommon partition;

FIG. 7 illustrates an exemplary interest graph;

FIG. 8 is intentionally left blank;

FIG. 9 illustrates one method of allowing a user to sort a number ofbrands to define likely demographic characteristics for a persona inaccordance with an embodiment of the disclosed technology;

FIG. 10 illustrates one method by which likely demographiccharacteristics for a persona can be determined based on brand sortingby a user in accordance with an embodiment of the disclosed technology;

FIG. 11 illustrates how a selected persona defines a number of likelydemographic characteristics that can be selected by advertisers todetermine a target audience for advertisements in accordance with anembodiment of the disclosed technology;

FIG. 12 illustrates one representative method of determining a targetaudience from the likely demographic characteristics of a number ofpersonas in accordance with an embodiment of the disclosed technology;

FIG. 13 illustrates one embodiment of a system for deliveringadvertisements to a user's computing device in accordance with thedisclosed technology;

FIG. 14 illustrates further detail of a system for selecting anddelivering advertisements to a user's computing device in accordancewith an embodiment of the disclosed technology;

FIG. 15 illustrates one embodiment of a representative user interfacescreen displaying a persona's email program;

FIG. 16 further illustrates one embodiment of a representative userinterface screen displaying a persona's email program;

FIG. 17 illustrates a block diagram of a user's computing device inaccordance with an embodiment of the disclosed technology; and

FIG. 18 illustrates one embodiment of a networked computing system usedin implementing the disclosed technology.

DETAILED DESCRIPTION

Profiles and Interest Graphs Primer

As discussed in previous patent applications, advertars, personas andprofiles of a user may reflect demographics/characteristics andassociated probabilities of a user actually having saiddemographics/characteristics among other information. Interest graphsprovide a valuable tool to represent this information in a computingdevice. As the user sorts brands and inputs Swote™ input, provides otherinformation regarding their likes or dislikes, or other information,profile data that is reflected in a representation via interest graphsmay be created or supplemented as illustrated in FIG. 1. FIG. 7illustrates one embodiment of an interest graph. In this embodiment ofan interest graph, there are nodes 708 that represent a particularinterest or other data and links that extend between various interestgraph nodes that represent propensities of the interests.

As opposed to a social graph (which may also be included or maycontribute to a profile) an interest graph focuses on shared interestsregardless of personal connections while a social graph focuses onconnections based on personal connections. (In some embodiments,profiles may incorporate social graphs as well or just social graphsalone).

In one embodiment, the nodes of an interest graph refer to the specificand varied interests that form one's personal identity, and the linksrepresenting statistical probabilities attempt to connect people basedon those interests. Individually, this may mean different things oneperson is interested in—be it jogging, celebrity gossip, or animalrights—that make up likes and dislikes, and what has more meaning tothem over someone else. On a broader scale, the interest graphrepresents the way those interests form unspoken relationships withothers who share them, and expand to create a network of like-mindedpeople.

While the social graph consists of who a user knows, the interest graphconsists of what they like, what moves them, and the facets of theirpersonality that, in part, make up who they are. These interests can berepresented in an interest graph by an interest graph node 708 and theprobabilities, which may be propensities for the user toward eachinterest as well as propensities between interests themselves. Thesepropensities may be represented as link 706 may also be incorporatedinto interest graphs. These connections can be much stronger, and muchmore telling, than simply who they are friends or acquaintances with.For example, two people being linked together because they knew eachother in elementary school or work at the same job doesn't necessarilyindicate anything about them beyond their connection to each other. Andfor the people involved, it doesn't always foster a very strong orlasting connection. As such, an interest graph may offer more insightinto each person's personal tastes, preferences and behaviors. Tofurther on FIG. 7, link 706 links the mountain biking node 714 to theuser node 702. The link may represent a propensity like 72% (any scaleor system can be used). Here, the 72% may indicate that there is a 72%chance the user 702 is interested in mountain biking. Expanding furtherin FIG. 7, link 712 links the heavy metal node 710 to the “ROCK!” node708. Link 712 may represent a propensity such as +40%. Here, +40% mayindicate that if the user 702 is interested in heavy metal, there is a+40% chance the user may be interested in “ROCK!” e.g., if interest inheavy metal then there is a −40% propensity to be interested in ROCK!.In another embodiment the probability may be a negative one such as−40%. This may indicate a level of “disinterest” user 702 has for“ROCK!”, For ease of illustration, nodes that overlap in this figure mayalso be interpreted as being linked.

Thus, given two connected users (such as user 702) connected in aninterest graph, the users likely are more interested in the sameadvertising as opposed to users who are not. In addition,characteristics and associated characteristics (e.g., via a taxonomy) ofthose users can be studied and offers, products and other goods/servicescan be developed specifically for those demographics. This provides ahighly personalized experience and also connects a user to users whohave characteristics in common. As illustrated, not only differentusers, but also a user's advertar such as 704 may be incorporated intointerest graphs.

The data used to create the Interest graphs may itself contain or beassociated to properties of the data. For example, in FIG. 7, “ROCK!”node 708 (optionally its probability links as well) may be tagged orotherwise associated with data properties 716. Various properties can bestored such as if the data is public, private, has generic information,has non-generic information, the node owner (here is Jill Roberts-theuser of this interest graph), statistical probabilities to connectednodes, products it is related to e.g., rock music songs and variousother properties. Properties can be assigned to any data including alink such as link 706.

Thus, interest graphs serve as a useful tool to represent personainformation. In addition to nodes and links, other tools such as tablesmay be used to represent interest graphs and the data the interestgraphs are based upon. The tools discussed herein may use interestgraphs or other tools to represent and manipulate any profile data. Theprofile herein may be stored in any number of ways such as a database,array, spreadsheet etc.

Central Partition Introduction

One advantage to aggregation of brand information in a central partitionis enhanced analytics. As illustrated in operation 3 in FIG. 1, in oneembodiment, central partition 202 may contain an interest graph, whosedata was aggregated from data obtained from a variety of sourcesincluding various brand owners' servers as well as from direct end userinput. Specifically, since this aggregated information was collectedfrom different brand owners, it likely has substantial consumerinformation that often cannot be collected from a single brand alone.Integration of data from multiple brands into a central interest graphoffers many possibilities in enhanced prediction of relevant contentsuch as expanding brand and central partitions accuracy via enhancedmarketing data/taxonomies/brand sorting/reverse brand sorting. Thisinformation about a user may be shared by the user or auctioned by theuser or a third party (e.g., the audience server owner) across brands.

Here, a central partition may be comprised of demographic data,characteristic/interest data, various data from user inputs, brand data,social graphs, contact information, friends, content such as pictures,posts by the user/friends, spend graphs, preferences, location, purchasehistory, browsing history (e.g., Personally Identifiable Informationsuch as contact information) or any other types of data. As used herein,a partition may be any data in logical memory. Examples may be any datagrouping such data randomly dispersed in a dataset across differentphysical partitions.

As discussed herein, a balance must be found between data sharing andproprietary data confidentiality. In one embodiment as discussed below,any piece of data in the central partition and brand partitions/profiles(e.g., interests graphs) may be tagged with permissions/attribution andstatistical probability contributions and then stored on various devicesconfigured for sharing with other partitions as in FIG. 7. This allowsvarious levels of data sharing via permissions/attribution and thusprevents co-mingling of data. In one embodiment, data may be stored indifferent partitions in which each partition is assigned desiredpermissions/attribution, which in turn regulates transmission/disclosureto others.

Exemplary Central Partition Creation and Use

In one embodiment as illustrated in FIG. 1, a series of operations areillustrated depicting an exemplary creation and then utilization of acentral partition in information sharing between brand owners and aconsumer user. First, a consumer user may create data for a centralpartition by first creating an account (e.g., a universal login) on aclient device 201 to be stored on a remote audience engine 214 or otherdevice. The user may then enter information in device 201 in a varietyof ways. As illustrated in operation 1, the input information mayinclude data created by a combination of brand sorting and/or Swote™input (as discussed in previous applications) or other tools such asentering information explicitly through keystrokes etc. (e.g., manuallyentering or selecting hobbies individually, using data imported frombrand servers via the universal login etc.). As discussed in theabove-referenced applications and also illustrated in FIG. 1, thesetools may be used to create a plurality of characteristic tags such asinterests, other users, brand preferences and demographics andassociated statistical probabilities, which may be represented by aninterest graph comprised of nodes and probability links as illustratedin operation 2 on an audience engine server 214. FIG. 10 illustrates anexemplary calculation of computing tags and associated probabilities andFIG. 1 illustrates an exemplary Swote™ input, both discussed in theabove referenced applications.

As illustrated, the central partition information may be represented viaan interest graph stored optionally within the central partition. Theorigin and contribution of each node or partition and probabilitycontribution may also be tracked by attribution tags or other tools toindicate ownership.

Sharing information between partitions is also illustrated in FIG. 1. Inoperation 3, the user and brand owners may then access the above data onthe audience engine 214 central partition/profile 202 (assumingappropriate permissions have been granted). In addition, the brandpartitions/profiles 206 and 204 associated with the brand owner serversmay transfer or otherwise share information to the audience engine forinterest graph integration as needed. Also as illustrated, other usersmay access (with appropriate permissions) the user's data on variousdevices via a network.

As illustrated in the embodiment in FIG. 2, audience engine 214 itselfmay administer information sharing, transmission, reception and otherdata usage for a partition/profile(s) containing user data. The audienceengine or other devices storing the partitions may be given or madeaware of data usage permissions for specific pieces of data (e.g., datarepresented by nodes and associated probabilities) by the end user, thebrand owner, the audience engine owner or a combination with any desiredpermission hierarchy. Said permissions not only serve to protect theuser from revealing information she does not want to reveal, but alsoserves to protect non-generic brand owner data from other brand ownersor other entities. In some embodiments, the individual partitions orindividual portions of each partition of data may be tagged as above aswell as any combination of these.

Expanding on the above, in one embodiment, the brand partitioninformation may be copied from a plurality of brand owner privatepartitions to an audience engine server 214. Each of the brandinformation profiles is then optionally stored in its own partition orprofile with the desired permissions (e.g., not accessible by otherbrand owners and users). The brand profile partitions/profiles are thenlinked to the central partition/profile upon user request as illustratedin FIGS. 1, 2 and 3. In operation 3, sharing information from thevarious private partitions can be shared in raw or generic form asdiscussed below.

As illustrated in operation 3, one of many contemplated data sharingconfigurations may be between the audience engine 214 (audience enginememory) and the brand owner server(s) 210 and 212 and may be executed byeither the user/brand owner configuring via webpage login on theaudience engine or by the user/brand owner configuration while loggedinto the brand's servers from a remote device. The data sharing betweenthe central and brand profiles may be a one-time exchange of informationor continuously updated as the data if either one of the partitionschange. In one embodiment, the central profile partition and the brandprofile partitions may be on the same device such as an audience engine.Brand owners may access and store their partitions on the audienceengine 214 or copies may be made on brand owner servers for latencypurposes.

A brand profile/partition 204, 206 may be associated with an existinguser account at the brand owner's server such as a credit card account,bank account, customer account (e.g., a Nordstrom credit card account).The partition may contain non-generic/private information proprietary tothe brand owner. In one embodiment, the brand account may be a simpledatabase of purchases, browsing history, PII, contact information andother customer information. The brand partition may also have datastored in interest graph form.

The central partition may also contain or may be linked to a partitionsuch as sub-partition 406 in FIG. 2. The sub-partition may be comprisedof information from the brand owner partitions in raw and/or genericforms as discussed below. The sub-partition may focus on a specificinterest (e.g., advertar/persona) of the user such as skiing, cooking,work, a brand profile or a demographic profile etc. Copies or at leastpartial copies or access to these copies of these sub-profiles may beshared with a brand owner server, user or other entity. Its permissionsmay be the same or different than the central partition and brandowner's private partitions, which are also discussed below.

In operation 4, the shared information from operation 3 (such as thedata contained in a sub-partition) may be used as discussed in previousapplications—such as to make content recommendations to a consumer enduser, conduct reverse brand sorting, enhance brand owner partition dataetc.

Partition Structure and Configuration

FIG. 3 illustrates a similar embodiment to that occurring within FIG.2's audience engine 214. Specifically, it illustrates data asrepresented via interest graph nodes and links. Central partition 202 isillustrated as a separate partition from that of private brand ownerdata partitions 204 and 206. These private partitions feature nodes 308and 310 respectively and are connected by probability links. The centralpartition maybe configured by the audience engine owner to be accessedby both the first and second brand owners that have access to 204 and206 respectively. As such, data contained therein such as the nodes 304and associated links 306 may be accessed by said brand owners. Thus,this central partition may function as a common source of informationaccessible by the brand owner's servers 210 and 212. As discussed below,central partition 202 may be optionally configured to exclude otherbrand owners.

As discussed above, a brand owner may wish not to expose their privatenon-generic information to others. Thus proprietary information may bekept confidential from other brand owners and optionally the owner ofthe audience engine 214. For example, in this embodiment, privatepartition 204 has data represented as nodes 308 and 312. These nodes andassociated statistical probability links connecting them, containinformation that the brand owner wants to remain private. As such, viapermission tags or other tools, the data for these nodes and links isnot visible or otherwise accessible to at least the second brand owner.In turn, data partition 206 is configured in a manner similar to datapartition 204.

As illustrated in FIG. 3, the three partitions 202, 204 and 206 asillustrated are connected by links such as statistical probability linksor other relationship links to a central node 302 via connecting nodes312 and 314. As illustrated in FIG. 4, nodes 302, 312 and 314 areconnected by links 408. The central node 302 in partition 202, may be anidentifier of the user's profile, a characteristic, interest, location,brand, product, coupon, offer, ad or other data. In one embodiment, itis an anonymous identifier associated to an end user such as a iPhone™application installation ID installed on her device such as her mobilephone. In another embodiment, it may just be an interest such as “frozenfood” or “road biking” and the associated links may be associatedstatistical probabilities.

Connecting nodes 312 and 314 and the links 408 that connect them, serveto associate their respective private partitions to the centralpartition 202. The data within nodes 312 and 314 may or may not be knownby the audience engine 214 or others. These nodes may be used forpartition association purposes as discussed below, which may includepartition interchanges. Connecting nodes 312 and 314 may be configuredor based on data the same as node 302, or each may be different asdesired. For instance, node 312 may be an anonymous ID associated to theend user's customer account on the brand owner's server or elsewhere onthe audience engine. This specific integration may ease integration intothe brand owner's databases. In another embodiment, nodes 312 and 314may be a category of product or other commonality (e.g., the same tag ordata property 716) that the two partitions have in common e.g., a“frozen food” node as discussed below. In other embodiments, centralnode 302 may have other nodes connecting partition 202 to other privateand non-private partitions. The private partitions and central partitionin turn may also have multiple nodes connecting them to any number ofother partitions. Linking between partitions may use the illustratednodes and links. If the data are not represented as interest graphs, anysimilar tools that link data partitions together may be used.

FIG. 4 illustrates an exemplary calculation of new information in newlycalculated data sub-partition 406 with nodes 402 and 410. Sub-Partition406 may be based on central partition 202, data partitions 204 and 206or any combination thereof including other partitions. The newinformation may be comprised of genericized information using thegenericization tools discussed below. The use of the genericizationtools below means that sharing sub-partition 406 with others does notcompromise the privacy of private partition data it was based upon.

Genericization of Private Partition Data

Once the various brand owners have established their private partitionson, or otherwise associated them to, audience engine 214, the datawithin the partitions may be directly or indirectly shared betweenpartitions as illustrated in FIG. 4. Specifically, generic data based onnon-generic data may be conditionally authorized by one privatepartition owner to be shared with another private partition owner orothers.

Genericization of private information such as that in private partitions204 and 206 is an important tool to allow such levering of non-genericproprietary information. Genericization may remove information such asidentity information and/or substitute private information to make theinformation substantially common/generic. Such approaches may be viaremoving, substituting or altering non-generic data such as specificreferences, brands, content, statistical probabilities, time, type,characteristic, demographic, size (e.g., large shirt size), color (e.g.,dress color), origin, property, identity, dates, locations,relationships and other data that is substantially private, data whichin combination is substantially private or data designated as private bythe brand owner.

Exemplary Genericization

FIG. 5 illustrates one embodiment of genericization of two privatepartitions 502 and 504. Here the private partitions are owned byWal-Mart™ and Costco™ respectively. Given that in many product spacesthey are competitors, they are typically reluctant to share any datawith each other-especially non-generic data that may give the other anadvantage. Specifically, they have invested significant time and moneycollecting their data and wish to keep this information confidential butstill wish to leverage their private information in exchange for other'sinformation. Thus, genericization into a separate partition orsub-partition allows this type of data leveraging between competitors.

The data in private partitions 502 and 504 may be based on specific userinputs such as Swote™ inputs, brand sorting, direct input, user purchasehistory etc. As illustrated, this data was based on a Swote™ input froma specific end user about her food preferences and interests gathered bythe Wal-Mart and Costco directly from their customers. In otherembodiments, the partitions may comprise entire customer bases,customers with a specific interest, a specific demographic in common, alocation in common or other commonality e.g., any types or grouping ofdata desired.

In operation 1, the private partitions 502 and 504 feature variouspieces of data, which may be in any database format on a single ormultiple physical partitions grouped by a logical partition etc. Theinformation may optionally be represented as an interest graph such asthose shown in FIGS. 3, 4 and 6. In such case, Stouffers brand andWal-Mart brand 508 and may be represented as nodes themselves andconnected to their various products illustrated and their associatedprobabilities with interest graph links. The products 510 themselves mayalso be represented as nodes and their probabilities as links connectingthem to other nodes e.g., Stouffer's Pizza node associated with thestatistical probability link “+0.2”. Probabilities may also have anegative value, which indicate a lessor end user interest.

In operation 2, the data from private partitions 502 and 504 areexamined for commonalities and then grouped by said commonalities (suchas grouped/tagged by associated SIC, GICS, NAICS codes discussed below).This may be done for any desired commonality and at various hierarchylevels as discussed below. As illustrated, grouping is done first at acommon product category level (e.g., frozen foods category) and then acommon product grouping (e.g., two pizza products). Grouping may be donevia: any characteristic, demographic, property, description, type,location, usage, brand, ingredient, discount, common tag, content or anyother data that may be desired for grouping.

In regards to a first level of commonality, in this embodiment, the datamay be examined for a common tag or other property at a common productcategory level. For instance, Stouffer's Pizza, Chicken, Nachos and LeanCuisine Pizza, Steak and beans share a common property tag in that theyare all tagged with “frozen foods”. These may be evident by data inprivate partitions 502 and 504 e.g., the products were tagged as such bythe brand owners. Alternately, audience engine 214 may examine eachproduct and determine appropriate tags from this commonality by taxonomy512, marketing data, a human marketing expert, NAICS, SIC, GICS codesetc. Here, the products may be tagged with “frozen foods” category 514and grouped together as in the table in operation 2.

After a first grouping by commonality, individual products between thetwo partitions may be optionally examined and grouped by furthercommonality to as many hierarchal levels as desired. This may be donethrough text matching or taxonomies or other tools. In the former case,the “Stouffer's Pizza” and “Lean Cuisine Pizza” shared the common textidentifier “Pizza”. Thus, given this further commonality of these frozenfoods in addition to both of them being frozen foods, these two productsare grouped in the same row in operation 2.

Once the two pizza products are grouped together, their information issplit/separated into generic 520 and non-generic 518 portions. In oneembodiment, a commonality between data from the two different partitionsmay be used as the generic data 520 e.g., “pizza”. As illustrated,generic data 520 is chosen as “pizza” since both Stouffer's and LeanCuisine (data from different private partitions that have been groupedtogether) both offer a product with text the test descriptor “pizza”. Inturn, the remaining information—non-generic information e.g., theirbrand names Stouffers and Lean Cuisine are grouped together in nongeneric data 518. These are the differences in data from the commongrouping between the two different partitions. Alternately, Stouffersand Lean Cuisine Pizza both have a “pizza” tag, their common tag “pizza”itself may be designated as generic (e.g., partition data subsequentlytagged as “pizza”) as well in addition to their composite probabilitiesdiscussed below.

A taxonomy 512, such as a semantic map and/or marketing data may alsoselect generic data 520 e.g., generic data from a taxonomy table itself.For instance, if a taxonomy table relates different brands of pizza to acommon “pizza” category, the generic “pizza” category tag in thetaxonomy table can be used as generic data 520. Alternately, a list ofbrands and other non-generic information can be compiled and used todetermine non-generic information. In operation 2, “Lean Cuisine” may bebelong to such a compilation as non-generic information list but not theterm “steak”. Thus, “Lean Cuisine” may be designated as non-generic and“steak” as generic.

Generic data 520 and groupings, data tagging may be selected bymarketing data, a taxonomy, semantic map, a human marketing expert orother tools such as Standard Industrial Classification (SIC)http://en.wikipedia.org/wiki/Standard_Industrial_Classification, GlobalIndustry Classification Standard (GICS)http://en.wikipedia.org/wiki/Global_Industry_Classification_Standard andthe North American Industry Classification System (NAICS code).Determination of what data is generic/non-generic may be via the toolsabove as well. Also illustrated here, in the table in operation 2, an ID516 is assigned for identification and optional record keeping purposes.

Also illustrated in operation 2, data within the same grouping (such asgrouped by SIC, GICS, NAICS codes above) above have their associatedprobabilities grouped together in a composite probability 522.Specifically, their associated probabilities can be combined orotherwise used together. Specifically, Stoffer's Pizza had a probabilityof +0.2 and Lean Cuisine had a probability of +1 in their respectiveprivate partitions. In this example, their composite value is thencalculated by 0.2+1=1.2. Various formulas other than a summation may beused as well as weighting different probabilities as desired. Someexamples are weighting a probability considering number of users it wasbased on, date range the information was collected etc. As illustrated,the other products such as Steak, Chicken, Nachos and Beans had nosubstantially generic data counter parts so their probabilities remainseparate from the other brand's products but are still grouped in the“frozen foods” category 514.

In order to present the generic information and their associatedprobability to other users without revealing proprietary data, the“non-generic” data 518 column from operation 2's table are separated.The generic data column and their associated probabilities are theninserted into a partition such as sub-partition 506 or any otherlocation as desired. Specifically, in operation, 3, associatednon-generic data 520 and ID 516 columns are stripped off the tableformed in operation 2 to preserve privacy of the brand owner's data.

This new generic data in sub-partition 506 may be kept in a separate subprofile/partition as illustrated in FIGS. 2, 4-5 or integrated intocentral partition 202. New permissions and propensities may be set onthis new generic data partition as discussed herein or they may be giventhe same or similar permissions as that of data with central partition202 or one or more of the partitions the generic partition was basedupon. This information in the generic sub partition 506 may also berepresented as a user interest graph and or used for productrecommendations and other previously discussed uses in the abovereferenced applications.

Various other embodiments are contemplated in which any number ofprivate partitions and/or commonly accessible partitions may under gooperations 1-3. For instance, commonality grouping and probabilitycalculations like the above may be based on any number of privatepartitions using genericized or raw non-generic data, data commonlyavailable from a taxonomy or marketing information, data commonlyaccessible in central partition 202, etc.

In one embodiment, private non-generic information may be substitutedwith a substantially similar brand (such as those in Standard IndustrialClassification (SIC) groupings e.g., data tagged with the same SICcodes) information to preserve brand owner confidentiality. Forinstance, instead of a generic category name like “pizza” replacing“Stouffer's Pizza”, a similar brand (with similar marketing data asevidenced by substantially similar tags such as customerattributes-demographics, product offerings, content, locations etc.) tothe “Stouffer's” (e.g., Godfather's Frozen Pizza Brand) may besubstituted in place of it. Replacement may comprise the associatedproducts probabilities, generic data etc. from that replacement brand.This substitute brand data can thusly be used in lieu of “Stouffer's”brand which in this case was private data.

In another embodiment, a brand such as Giant Bicycle brand is containedin a private partition. If no substantially generic brand issubstantially equivalent to this brand, then a variety of tools can beused to protect the proprietary brand information “Giant Bicycles” andits associated probabilities and products. For instance via a taxonomy,marketing information or other tools can be used to replace “GiantBicycles” with a generic category such as “bicycles”. This genericcategory may be populated by bicycle data (brands although that no onebrand alone is suitable for direct replacement of “Giant”). A pluralityof products and composite probability values together across a pluralityof brands may be a suitable substitute for “Giant”. Thus, from thesebrands, associated probabilities and products can be genericized acrossa plurality of existing brands/products and probabilities by the toolsabove to produce a substantially suitable substitute. E.g., usinggeneric data for the brands/products and averaging their probabilitiesto genericize the base data into data substantially suitable forreplacement of the “Giant” data. For example, to find a substitute for“Giant”, “Giant” may be associated to various NAICs industryclassifications. Companies found to be in the same industryclassifications may have their information (probabilities and otherproperties) aggregated together and a composite brand created forsubstitution for “Giant”.

In another embodiment, private or generic sub partition data may beexamined before or after any of the tools above are used. Examinationmay be for privacy sensitive information by examining the data, tags andprobabilities that may lead to the user/brand being identified. If thedata belongs to a list of known sensitive information, keywords, thereis a probability that someone could use the related data tocontact/discover the user/brand, related financial/location information,family or related user or workplace information, items bought togetheretc. After such examination, the data determined as sensitive can bestripped from the shared interest graph/profile/partition and, replaced,substituted as suitable etc.

Leveraging the Central Partition Via Actor Identity PartitionInterchangeability

FIG. 6 represents an alternate view of the data discussed in FIGS. 3-4.This view illustrates the ability to the leverage central partition 202across a variety of brand owners while keeping their proprietarypartition data private. Said use being the ability to interchangepartition 202 with various private partitions (e.g., private partitionowned by Wal-Mart or that of Costco) depending on the brand owneridentity. This interchange of private partitions provides the ability toprovide context to the private partitions in view of the centralpartition 202 with minimum if any retooling/repurposing of the centralpartition 202.

Central Partition 202 (with a partial illustration of its nodes andlinks) is represented as having an interchangeable section 602. Subpartition 406 was calculated in a similar manner to that discussed inFIG. 5 e.g., it was genericized from two private data partitions such aspartition 204 and 206 etc.

Section 602 is an interchangeable area in the central partition wherethe partition 202 can interchange various private partitions such as 206and 204 as well as the newly calculated generic data sub partition 406.Section 602 may be defined as a section of the central partition 202connected to data in the central partition which as been tagged (orotherwise designated) tags to indicate that they will be connected orotherwise associated to an interchangeable partition.

The particular private partition selected for insertion into section 602may be selected by the audience engine or other device according to theidentity of the entity viewing it and associated access permissions.Specifically, a partition that a brand owner has permission to, isselected via the partition's data properties such as ownership andpermission tags. More, specifically, while central partition 202 ispresented to the owners of private partitions 206 and 204, theirparticular private data partition that was previously selected by dataproperty tags is presented in place of each other such that a non-ownerwill not see another owner's private partition. However, theirrespective partitions will be presented with central partition 202 togive viewer with appropriate permissions context and enhancecalculations across these partitions.

Thus, partition 202 and a selected partition (e.g., 206, 204 or 406, acombination of these or a new partition based on any of thesepartitions) can be and presented as a whole partition to the owner ofthe private partition or other entity that is given access to theselected interchangeable partition and partition 202. Presentation ofthe plurality of partitions may be made transparent to the owners of thevarious partitions. For instance, in one embodiment, an entity viewingits own partition and partition 202 together may not even be able totell that section 602 exists and the existence and extent of otherowner's private partitions.

Exemplary Interchangeability Mechanics

As discussed above and illustrated in FIG. 6, central node 302 isillustrated as connecting partition 202 to the newly calculated subpartition 406 in section 602. Central node 302 may be connected to anydesired node in sub partition 406. These may be connected via links 408.For illustrative purposes these links 408 are also illustrated in FIG. 6as triple lined connectors.

If the embodiment in FIG. 5 was viewed in a manner similar to that ofFIG. 6, private partitions 502 and 504 may be interchangeable with acentral partition similar to that of central partition 202. A centralnode(s) in the central partition as discussed above may link the variousprivate partitions and sub-partitions together as desired (e.g., linkedtogether by nodes that the partitions have in common). Connector nodesin the private partitions may be any node but here are illustrated asnodes representing commonalities between the private partitions such as“frozen foods”. Thus when partitions are interchanged, the interchangerepresents different “frozen food” information specific to differentpartition owners such as Wal-Mart and Costco as indicated by dataownership property tags e.g. data properties 716. Nodes and linksattached may be products and probabilities 510.

Specifically, the Frozen Food category 514 or other common data such asa frozen food node and associated probabilities may be inserted into thepartitions 502 and 504 and sub partition 506. This serves to provide apoint of reference for partition interchangeability. Thus, the frozenfood nodes tagged as interchangeable nodes corresponding to frozen foodinterchangeable nodes in other private partitions would be a point ofinterchange between private partitions via central node 302. Inaddition, sub-partition 406 may also feature a “frozen food” node forthe same reason given the partition is a genericized version of frozenfood information from the private partitions 502 and 504. This frozenfood node in sub-partition 506 may also be connected to the generic data520 and associate probabilities 522.

In a like manner in FIG. 6, three nodes in three different partitions,node 402, 314 and 314 were chosen to be the nodes which interchange withpartition 202's central node 302. The type of interchangeable partitionmay be determined by common category, content, characteristic, brand orany other grouping. A plurality of interchangeable partitions may alsointerface with one or more nodes in partitions 202 in a similar manner.

As discussed above, this partition interchangeability in context withpartition 202 provides meaningful contextual information to the privatepartitions and generic partitions. As discussed below, a party's abilityto view various partition may be contingent upon being granted accesspermission in response to it contributing certain information in return.

Partition Access Permissions

Permissions are an important aspect for data privacy not only for theend consumer user, but also for the brand profile owner. In the latercase, confidentiality and a prevention of data co-mingling is importantas a particular brand owner may not wish to share their specificinformation (e.g., raw information) with another specific brand owner.However, a brand owner may be willing to share the generic informationin partitions like 506 above as well as generic information in thecentral partition 202 with other brands. But before such sharing isagreed to, a brand owner typically needs assurance that the brand beinggranted access rights is in itself contributing some substantiallysuitable information in return for access.

As such, various permissions maybe assigned to the partitions 206, 204and 406 as well as to other partitions and even individual pieces ofdata such as individual nodes and probabilities or data that theserepresentations are based upon. Permissions may be set by the audienceengine at the request of the private information contributors or set bythe device storing the particular partition at issue. Permission settingmay be through tagging partitions, portions of partitions or each pieceof data such as a node/link with permission tags. For instance, noaccess permission to anyone but the owner may be assigned to a privatepartition. Permissions may comprise: access, read, write, execute,delete, move, copy, rename etc. Various partition users can be assignedwith differing permission levels as desired such as based on, forexample, data contribution level. For instance, access may be grantedproportional to the contribution of information made available to otherbrand owners in raw form or generic form.

Equity Criteria

Determining if permissions and to data may be granted based upon avariety of criteria. One such access criteria is if an entity requestingaccess to a sub-partition, central partition or other partition isoffering data that is a substantially equitable trade.

In the above case, an owner of a private partition may wish to ensurethat an entity requesting access will exchange substantial informationby providing a substantial or substantially similar amount of raw data,genericized data or a combination thereof (their offer data) in returnto access to the private partition.

To determine if a data exchange like that of the above is indeedsubstantially equitable, various tools can be used. For instance, a dataowner may require that the other owner's data proposed for exchange be:comprised of a certain number or range/threshold of users, comprised ofa certain number/threshold of nodes, have particular links or otherdesired pieces of data, be comprised of certain categories of data shedoes not have or has substantially less of, does not have asubstantially large amount of the same information (e.g., categories,content, brands) or equivalent information (as determined by marketingdata, taxonomy, marketing experts etc.), be of similar value in terms ofbeing evaluated from a monetary perspective (data as apprised by variouswell known industry techniques and/or industry experts to be worth acertain dollar amount or virtual currency amount) or threshold amount(e.g., gigabytes of data) or granularity (e.g., in terms of desireddetail), the type, number of other brand owners contributing to therequested data, the contributing brand owner's data relevancy to theoffered data, the requesting brand's past contributions, the requestingbrand's reputation in the relevant market, financial value of theinformation (e.g., higher contribution if data is about an expensiveproduct item), age of the information, the reputation of the brands,characteristics of the brands, probability ranges (as discussed below)etc. Any or a combination of the above may be used for the same ordifferent potions of the data in question in either the private and/orcontributor's partition.

In one embodiment, all the contributors to a sub partition such as 406may layer their permission criteria together. Thus a user wishing accessto partition 406, may have to satisfy all their permission criteria orthe most restrictive of the criteria are overlapping.

Equitable Data Exchange Embodiments

In one example of equitable data exchanges, as above, private partition204 was used at least in part to create sub-partition 406. The owner ofpartition 204 wishes only those with substantially similar data (or dataabove a desired granularity threshold) to be able to accesssub-partition 406. In turn, sub partition 406 is configured to beaccessed only by authorized users that meet such criteria.

A third party entity who wishes to view sub-partition 406 submits theirdata to exchange, which may be raw data, or data genericized via thetools above or any other data or a combination of the aforementioned.The data is examined/compared by the audience engine 214 or other devicesuch as a neutral third party device. The offered data may optionallynot be accessible to the owner of partition 204 and a neutral party mayserve as a data “escrow” for the exchange. If the third party offereddata meets the desired criteria as set by one or more of thecontributors of sub-partition 406 or optionally, the audience engineowner, then the data in sub-partition 406 may be shared with the thirdparty entity and in turn, the third party entity data shared with theowner of partition 204 or others that have contributed. This accessgrant may be done by reconfiguring sub-partition 406 e.g., dataproperties to allow third party viewing but not grant other permissionssuch as write permissions. In another case, if only partial criteria ismet, then an amount of data proportional with the partially satisfiedcriteria may be shared with third party entity. Proportions may bemeasured in quantity, quality of data, granularity, number matchingcategories etc. In one embodiment, a comparison is done between onepartition, or a generic partition created from another partition to theoffered data. A determination of an increase in detail (granularity) isdone to determine if the owner of the partition/generic partition isgetting additional data from the offered data.

Once the third party is granted access to sub-partition 406, it may viewit as illustrated matched with central partition 202 (assuming it alsohas been granted access to it as well) together for contextual purposes.

In one embodiment a brand owner may configure the tools above remotelyvia the audience engine 214. Specifically, as discussed above, afteruploading their partition data such as tags, probabilities, affinitycontent inputs (e.g., brand sorting or Swote™ input) content or anyother data such as end user data, the brand owner may login to theaudience engine and exclude others from partition access containingtheir private data. The user may then configure sharing as discussedbelow. For instance, upon receiving offer data from another user whodoes not have access to the private partition, the data may be analyzedas discussed above by the audience engine or others. Upon the offer datameeting criteria that the brand owner specifies such as the variousequity criteria discussed herein, access to the private partition isgranted to the requesting user. The offer data is then shared with thebrand owner in return. The brand owner's partition and the offer data atany time may contain tags representing an end user (user enteringaffinity content) input characteristic and associated probabilities.These may be configured as interest graphs and content recommendationsexecuted as discussed in the previously referenced patent applications.

Multi-Partition Calculation Embodiments

Once a third party entity receives access to a sub-partition such as 406and a central partition 202, various calculations may be executed. Forinstance, as illustrated in FIG. 6, product recommendations may beexecuted by using both partitions 202 and 406 together. These mergedpartitions may serve to execute product recommendations and deliver themto a user as discussed in previous patent applications.

In one embodiment, the owner of partition 206 may use partition 206,partition 202 and sub partition 406 together or various pieces of themtogether. Specifically, it may instruct the audience engine to combinethese together for a product recommendation or for other purposes.Specifically, the tags and associated probabilities of the desiredpartitions may be merged or otherwise manipulated like that in FIG. 5.In one example, the nodes tagged with pizza in both the privatepartition 502 and sub partition 506 can be grouped together andprobabilities combined through any desired calculations in a mannersimilar to that as described above. This adds the information to privatepartition 502 from sub-partition 506. A new partition can then becomputed and viewed with central partition 202 as desired.

Probability Range Embodiment

Probability Ranges. Inclusion or omission of data from a private,central or sub partition to a new generic partition may be decided withthe aid of probability ranges. In addition, probability ranges may beused to determine the quality/relevancy of data that is offered fortrade and thus may affect a determined value of the data.

For instance, data may be worth less thus not exchanged at all if theirnodes are within a particular probability range that infers that theyare substantially similar to the owner's own data. Data withprobabilities ranges that are substantially too low may be designated asnot relevant enough to a particular brand owner and thus worth less in adata exchange. Data with probabilities with ranges that aresubstantially too high may be determined as too irrelevant e.g., tooobvious. On the other hand, the same data with substantially high rangesmay be designated as high value and substantially relevant. Variousranges, thresholds and scales are contemplated and may defined throughthe above tools e.g., marketing data, taxonomies, marketing experts,past user data specific to the specific user or segment of users inquestion or other tools.

Technical Problem Solved

As discussed in this document, the discussed subject matter solvesseveral technical problems. Specifically solved, is the current problemthat users such as brand owners as well as end users lack an ability toeasily, equitably and anonymously/generically share their information.Therefore, what is disclosed are enhanced and flexible tools to enableusers such as the above to share non proprietary data in a substantiallyequitable manner.

The tools above may be used on any computing device and combinations ofcomputing devices connected to each other as illustrated in FIGS. 2,11-13 and 17-18. An advertar may be initially created by receiving inputfrom a client device and stored in memory, altered and processed on alocal or remote computing device or a plurality of devices in includingthe client device. Ads and advertar related information can be input andoutput to these devices from third party computing devices connectedover a network.

Appendix A

Persona Primer

As will be discussed in further detail below, the disclosed technologyallows users to create personas (also referred to as “advertars” or“advatars”) to serve as a privacy screen or a barrier between a user andadvertisers. In addition, the disclosed technology can serve as a toolto segment a user's interests/communications. A persona may berepresented as an icon or other symbol that can be selected by a userand has a number of characteristics (e.g. demographic characteristics)associated with it. The demographic characteristics may represent eitheractual or desired demographic characteristics of the user. Thedemographic characteristics associated with the personas can be used byadvertisers to determine a target audience for one or more ads. In oneembodiment, ads are delivered to a persona but the advertiser does knowthe identity of the user associated with the persona. Users may havemore than one persona that can receive ads. More than one persona can beactive at any time or one or more of the user's personas may beprogrammed to become active based on the time of day, location of theuser, current activity of the user, and proximity of the user toobjects, other users or locations or other factors.

Personas can be created by the user, copied from other users who havedefined their personas or adopted from one of a number of predefinedpersonas. In one embodiment, the demographic characteristics attributedto a persona are determined based on responses to the user's indicatedopinions such as likes or dislikes of a number of brands. As usedherein, characteristics may include the demographic characteristics of apopulation such as (gender, age, location, marital status etc.) as wellas properties, characteristics or traits relating to single individualusers such as a user's individual interests.

In one example a user who wishes to receive ads from one or moreadvertisers may use the disclosed tools. The user may select or create apersona that serves as a privacy barrier or screen between the user andthe advertisers. Ads are delivered to a logical address, such as to ane-mail address that can be accessed by the user's computing device toreceive the ads. In another embodiment, ads are delivered to a servercomputer (not shown) that forwards the ads to the user's computingdevice so that the user can receive the ads. The advertisers may notknow the identity or other personal information of the user other thanthe fact that the user's persona has one or more demographiccharacteristics that indicate that the user may like to receive ads ofthe type presented by the particular advertiser.

In one embodiment, a persona is implemented as a computer record thatrepresents an address or device identifier to which an advertisement canbe directed as well as a number of characteristics (e.g. demographiccharacteristics) that may be input directly by the user or inferred fromuser input. The aspects of a persona that can be seen by an advertisermay not identify the identity of the user such that the advertisercannot contact the user directly other than by the address or deviceidentifier associated with the persona. In one embodiment, a persona hasa graphic icon that represents the persona and a number of demographictags or categories representing the likelihood that the user falls inthat demographic category or wishes to receive ads that are directed topeople in that demographic category.

In one embodiment, separate cookies and caches are used for each personawhen using a web browser or other computing device. This segmentation ofpersona information prevents information cross over between personas. Inaddition, this segmentation gives context to the information in thecookies and caches given that all data is related to the persona'sinterests. This makes optional analysis of such cookies and caches morereliable since the user's activities only pertain to the selectedpersona. Optionally, the cookies and caches can be encrypted to protectprivacy.

FIG. 9 illustrates a method by which a user can indicate their opinionof a brand such as if they like a brand either more or less or feelneutral about the brand. As used herein, an opinion may encompass inputfrom any user interaction with or relating to the brand. Such examplesinclude if a user likes/dislikes, purchase/would not purchase, want/donot want as well as if a user is “following” a brand such as following abrand via Twitter™. In the embodiment shown, a user interface screen 900displays a number of icons 902 a, 902 b that represent recognizableconsumer brands. In the embodiment shown, the interface screen isdivided into three areas. A neutral area 904 represents a neutralfeeling about the brand (or unfamiliarity with the brand). An area 906is an area where the user places icons representing the brands they likemore while an area 908 is an area into which the user places the iconsthat represent the brands they like less. Icons representing a number ofbrands are initially shown to the user in the neutral area 904. Userscan then drag and drop the icons into one of the other areas 906, 908 toindicate that they like the brand more or less respectively.

In the example shown, a user has selected the icon 902(b) representingthe brand “Fendi” from the neutral area 904 and has dropped it into thearea 906 in order to indicate that the user likes this brand more. Ifthe user has no opinion of the brand or is neutral about the brand, theuser can leave the icon in an area of the screen 904 that groups iconsfor which no opinion has been expressed. Alternatively, iconsrepresenting brands for which no opinion or a neutral opinion isexpressed can be removed from the screen and replaced with another iconrepresenting another brand. Based on the opinions of the user to a groupof brands, an estimate can be made of the likelihood that the user hasone or more demographic characteristics (or would like to receive adsdirected to users having those demographic characteristics). In someembodiments, brands that are left or placed in the neutral area of ascreen may also be included in determining likely demographiccharacteristics in a variety of ways. For instance, if a user hasrelatively consistent neutral/unfamiliar opinion towards upscale brandssuch as Rolls Royce™ and Saks Fifth Avenue™, it may be inferred that theconsumer is neutral/unfamiliar to the brands because her income level islikely not in the range of consumers who are exposed to these brands.

In an embodiment, upon selection of a brand such as an upscale brand(e.g., Rolls Royce) an inference could be made that the user is ahigh-income user. In response, a subsequent brand sorting screen may bepresented with additional upscale brands to confirm the inference anddetermine other likely upscale demographic characteristics. Forinstance, if in the subsequent brand sorting screen, a user declinedselection or voted down of all of the subsequent upscale brands, then aninference would be made that the user is a “aficionado” of expensivecars, but not a “big spender” across different types of categories suchas spas, airplanes etc.

In the example shown, the brands represent known manufacturers orproviders of goods or services that the user can buy or use. However forthe purposes of the present application, the term “brand” is meant to beinterpreted broadly. A brand may include, but is not limited to, a logo,trademark, animation, text, movies, movie clip, movie still, TV shows,books, musical bands or genres, celebrities, historical or religiousfigures, geographic locations, colors, foods (e.g. packaged foods),flowers, animals, designs, characteristics (young, old, short, tall),emotions (angry, bored), political views, color combinations, shapes,graphics, sounds, movement, smells, tastes, slogans, social media users,personas, patterns, occupations, hobbies or any other thing that can beassociated with some demographic information. For instance any thingthat can be broadly accepted or recognized by a plurality of users canbe a brand. In addition, anything that can identify aseller/product/service as distinct from another can be a brand which mayinclude Huggies™ brand diapers, Copper River Salmon, Microsoft™software, a picture of Tom Cruise, a picture of a frame from one of TomCruise's movies, a musical band name, a musical band album cover, afamous picture such as the picture from Time magazine celebratingvictory in WWII in which a sailor is kissing a woman, a picture of ahouse in the country, a picture of a Porsche™ car, a picture of a smileyface as well as concept brands such as breast cancer awareness orenvironmentalism etc. In addition, a brand can be an abstract idea suchas “World Peace”, “Save the Whales”, political ideologies such as“Republican” or other concepts about which a user may have an opinion.

In one implementation, each persona is associated with one or more tagsrepresenting different characteristics such as different demographiccharacteristics. The association may be determined via the brand sortingduring persona creation. A tag may store or be associated with a valuethat represents the likelihood (e.g., a probability distribution) thatthe demographic characteristic represented by the tag is applicable to auser. For instance, the value of the tag may reflect a probability thatthe user is male while another tag represents the likelihood that theuser lives in New York. Other tags may store values that represent thelikelihood that the user has children, likes Chinese takeout food, andvotes Democratic etc.

Based on the user's indication of their opinion of the brands, such asif each brand is liked or disliked, the tag values can be combined intoa composite value that reflects that likelihood that the user has aparticular demographic characteristic. As an example, assume that a userindicates that they like Ford brand trucks, Remington brand shotguns andGolden retriever dogs, while another user indicates that they likeBarney's of New York brand clothes, Vogue magazine and miniaturepoodles. Here, the first user likely has a higher probability of being amale than the second user when one compiles the composite values of theprobability distributions associated to the gender demographicassociated to these brands. A different composite demographic can beassociated with the persona created for each user. A user may also reusecomposite demographics for multiple personas preventing repetitive entryof opinions. Advertisers then use these determined demographiccharacteristics to decide which personas should receive their ads.Brands may be selected for presentation to the user for brand sortingbased on the likelihood of a user having a certain a certain demographiccharacteristic. For example, selection of a cosmetic brand X likelyindicates a female user in which more brands relevant to females may bepresented.

In one embodiment, the composite demographic information is created fromthe group of brands that are sorted by the user based on her opinions ofthe brands. In the example shown in FIG. 10, a user indicates that theyshop for (e.g. like) brands 1, 2 and 4. The user has indicated that theydon't shop for (e.g. don't like) brand 6 and are neutral towards (e.g.don't like or dislike or are unfamiliar with) brands 3, 5, 7, and 8. Inone embodiment, the tag values representing the likelihood that a userhas a particular demographic characteristic are combined depending on ifthe brand is liked or disliked. In other embodiments, buy/not buy, wouldbuy/would not buy, use or would use, do not or would not use as well asother opinions or impressions can be presented alone or in combination.

In one embodiment of the disclosed technology, the tags for the brandsrepresent the same demographic characteristic. For example, Tag 1 forall the brands may represent the likelihood that the user is a malebetween ages 25-40, while Tag 2 may represent the likelihood that theuser is a male between ages 40-55. Tag 3 may represent the likelihoodthat the user is a woman between ages 18-22 etc. Each tag has or isassociated with a value representing the likelihood of a user having adefined demographic characteristic. These values for the tags aretypically determined from information gathered from consumers whovolunteer information about themselves and what brands they like,purchase etc. Such information is typically gathered from marketing datafrom consumer surveys or a variety of other data sources. The details ofassociating consumer demographic information with particular brands areconsidered to be well known to those skilled in marketing. In otherembodiments, users may assign a value to a brand by inputting the valueitself into the computing device, assigning a relative value to eachbrand and or tag (brand X given a higher preference to brand Y by givingbrand X a location assignment a screen above or to the right of brand Y)etc.

Not every brand may have the same set of tags associated with it. Forexample Brand 1 does not have a Tag 4, while Brand 2 does not have Tags2 and 6 and Brand 6 is lacking Tags 3 and 4.

In one embodiment, the composite demographic characteristics for apersona are created by arithmetically combining the values of the tagsfor the liked and disliked brands. In the example shown, Brands 1, 2 and4 are liked so their tag values are summed while Brand 6 is disliked soits tag values are subtracted. When combined as illustrated, Tag 2 has asummed value of 4.0 (1.5 plus 1.5 minus (−1.0)). A value of 4.0 for atag may represent a strong likelihood that a user has the demographiccharacteristic defined by the tag. On the other hand, a tag with acombined value of −2.5 may provide an indication that the user probablydoes not have the demographic characteristic associated with the tag andan inference can then be made. For example, if a composite gender tagvalue suggests the user is likely not a male, an inference can be madethat the user is a likely female. A composite of the values of the brandtags across the brands (e.g., the sum of statistical probabilities oftag A across brands X to Y as seen in FIG. 6) may also be represented bya vector that is associated with the persona. Each brand tag value inFIG. 6 may be a dimension of the vector.

In one embodiment, based upon the composite demographic characteristics,the corresponding user or persona may be placed into pre-computeddemographic segments. Such pre-computed segments are typicallydetermined from marketing survey data. Once the user is assigned to thesegment, additional associated characteristics of the pre-computedsegment may be associated to the user. In addition, ads that have beenspecifically designed to target the pre-computed segment may bedelivered to the user.

In one embodiment, an ad/offer/content that a persona may be interestedin receiving may be matched with the persona based on said personavector. Typically an ad comes with tags such as coffee, sale, spa,dancing lessons etc. Here, an ad/offer's tag values may be assignedbased on marketing data taken from consumer surveys such as aprobability distribution that a certain demographic (age, sex, incomeetc.) would likely desire to receive ads with a given ad tag. Thecomposite of ad tag values represent a vector for the ad. Each of theseoffer tag values may therefore be considered as an ad vector dimension.In one embodiment, tags related to the ad tags may be assigned alongwith their associated values to aid in ad-persona matching.

Once a persona is defined, a plurality of ads can be ordered forpresentation to the user according to likely persona affinity. Bycalculating the distance between the persona vector and the ad vector,such as their distances in N tag space, ads can be ranked in order oflikely persona desire. The result of this distance calculation may be aranked list of ads in order of affinity (i.e. the distance between thevectors) for a particular persona vector. In this manner, instead offiltering out ads, a relative ranking of ads is produced. Alternately,other distances between the ad and persona vectors (or any of theircomponents) can be calculated to produce a ranking. Various othermethods of ad filtering and ad sorting to match the appropriate ads tothe persona may also be used. In some embodiments, location, pastpurchases, sale times/items, membership in customer loyalty programs,percentage off and other factors may be used to aid in adordering/selection. In one embodiment, the calculated affinity for aparticular ad is displayed to the user as stars (e.g., an ad with ahighly calculated affinity is four our of four stars etc.). In anotherembodiment, the ordering/filtering may consider the ratio of thegeographic distance to an offer and the percentage off. For instance, ifan ad is only 10% off and the distance is several hundred miles from theuser, this ad would have a lower ordering then an ad that is 90% off andone mile away from the user. Here, the distance and percentage off etc.,may be displayed to the user as well. In yet another embodiment, thepersona may keep track of ads that resulted in a purchase by theconsumer. After a purchase, the user will not be shown the ad on thepersona that made a purchase or on all her personas.

Optionally, the dimensions on the persona vector and/or the ad vectorcan be normalized by multiplying the dimension by a scalar between forinstance, zero and one, to prevent particularly strong tag dimensionsfrom skewing the results.

In one embodiment, the composite persona demographic information isdetermined locally on the user's computing device with which theyindicate their preference or opinion regarding various brands. Inanother embodiment, the opinion information such as like/dislikeindications about presented brands are sent to a remote computingdevice, such a web server that determines the composite personademographic information. If sent to a remote computer, the remotecomputer can return a persona back to the user's device.

Audience Selection

In one embodiment, once a user has created or adopted one or morepersonas, the personas are registered with a server computer that maps apersona to one or more addresses or other identifiers to which adsshould be delivered. As discussed above, the address may be an e-mailaddress, IP address, device id., web site or another logical addressthat can be used to direct ads to the user.

As shown in FIG. 11, a selected persona defines one or more demographiccharacteristics 1100 (such as interests like Thai food) that may be ofinterest to advertisers in selecting a target audience to receive theirads. In the example shown, the persona “Jammin Out” has a +6 value forthe tag that reflects an affinity for Thai restaurants. Advertiserslooking for potential customers of Thai food, Thai restaurants, andtrips to Thailand etc. may search for personas having a relatively highnumber for this tag in order to select a target audience for their ads.

In addition, FIG. 11 illustrates a taxonomy expanding the user'sinterest tags. For example, the user has rated Thai Restaurants a +6. Assuch, the user would probably be interested in prepared foods in generalas well as Thai foods and perhaps even travel to Thailand. Theserelationships can be from user survey information. The new tags andassociated values can be assimilated into the persona. This expansion oftags provides the user the opportunity to see additional topics, brands,times, locations and other related information. In addition, a user maygive feedback on the tag's desirability and associated value.

FIG. 12 shows further detail of one embodiment of a system for matchingtag values for a number of personas with an advertiser's needs for atarget audience. In the embodiment shown, a user 1200 defines a numberof personas 1206, 1210, 1212 each having different tag values thatrepresent different characteristics such as demographic characteristics.The persona information is sent to an audience engine 1220 via a wiredor wireless computer communication link. The audience engine 1220 storesthe persona information in a database. An advertiser 1240 supplies theaudience engine with a list of demographic characteristics such as tagsand associated values they want in a target audience. These demographiccharacteristics are coded manually or with the aid of a computer intoone or more tag values 1242 or ranges of tag values. The database ofpersonas stored by the audience engine 1220 is then searched by thecomputer system to determine those personas having tag values match all,or as many as possible, of the desired demographic characteristics. Oncethe personas have been identified, ads 1256 are supplied fromadvertising companies 1260 to the audience engine 1220 that in turnforwards the ads to the addresses or identifiers associated with theidentified personas. Alternatively, third party advertising companiesand/or the audience engine 1220 may deliver the ads to the personas.

Ads may be displayed to users on the same device on which brand sortingoccurred or on multiple different devices. The ads may be shown on thesedevices within a specified amount of time or upon an event trigger suchas proximity to a merchant's store, the start of a sale, another userexpressing interest in the ad etc.

In FIG. 12, brands & advertisers can also gather personas from multipleusers. These personas can also be processed through steps 1 and 2 inwhich the yield is similar to the single user persona case but overmultiple users. In either case, an advertiser can determine audience orsingle persona/user trends, similarities in buying habits, and buyinglocations etc. Advertisers 1240 can get anonymous predictions (withoutuser identity) regarding predictions which are useful in displayingparticular customized ads, persona/user interests in ads and associatedproducts, or ordering inventory in anticipation of purchases. Typicallyan advertiser 1240 would be charged a fee by the audience engine 1220for displaying an ad and receiving marketing data pertaining to targetaudiences. In one embodiment, an advertiser or other party may analyzethe persona information to discover and target new audiences.

Audiences and personas may be accessed and transmit data to the audienceengine 1220 on multiple applications across multiple platforms anddevices. Typically each type of these interactions may communicate withthe audience engine 1220 using an identifier that represents the user'spersona. As such, simultaneously use of a single persona may bepermitted. Advertisers 1240 may be charged for varying access topersonas or audiences across various devices, platforms andapplications. For instance, an advertiser may be only permitted and thusonly charged to access certain personas in an audience using an iPhone™or access can be restricted to audiences using certain iPhoneapplications.

In one embodiment, the audience engine 1220 tracks the active time auser spends on each persona, actions/choices/votes/location/sharing ofads of the persona, ads voted on, purchases, click-thrus, impressions,advertising effectiveness, which application was used with the personaand which device(s) was used with the persona. This tracking may beconfidential and not revealed to third parties without consumerpermission. The user may be offered a reward such as money, points, giftcards in return for sharing this or other data. In another embodiment,the user may chose to share this data with selected personas owned byothers or herself which results in a real-time sharing of her actions.

In one embodiment, the demographic information associated with a personais refined depending on how the user reacts to ads delivered to thepersona or previous brand sortings. For example, if the user indicatesthat they do not like an ad, one or more tag values associated with thepersona may be adjusted. In this way a persona's determined demographiccharacteristics can be continually improved or updated. In oneembodiment, ads can be shown as icons and displayed and assignedaffinity/voted on in a manner similar to how brands are sorted asillustrated in FIG. 9. Answers such as “like the ad” “neutral” and“dislike the ad”, a picture of a “thumbs up” and “thumbs down” may bedisplayed on various screen areas so the user may know where to drag theicons to and thereby assign affinity to the ad.

In one embodiment, the feedback from user assigned ad affinity may makevery granular adjustments to a persona. In one embodiment, a simple voteon an ad may modify a plurality of aspects of a persona by consideringthe specific tag, subcategory tag and associated weights among otherthings. For example, an ad was voted “thumbs up” and the ad had thefollowing tags and associated values: car=1, car/Ford=0.2 andcar/Toyota=−1 wherein car is a category tag and Ford and Toyota aresubcategory tags. The persona could be modified in a plurality of ways.First, the persona would favor these tags and subcategory tags in agreater absolute magnitude than if the ad was voted “thumbs down”. Thisprevents undue voting down because users are more expressive aboutthings they like as opposed to things they don't like. Second, a varietyof tuning factors may be applied to the tags “car” or subcategory tags“Ford” and “Toyota”. For example, categories may not all be weightedequally. In one example, categories may be weighted differently fordifferent cultures. For instance, the automobile category may receive ahigher weight in US culture as opposed to cultures where automobileownership is lower.

If an ad was assigned a negative affinity, the tag and associated valuesmay play a lessor role in assigning ads in the future. In oneembodiment, no ads with those tags or related tags might be shown to theuser. In another embodiment, ads with these tags and related tags mightbe decreased but reintroduced to the user at a gradual rate to ensurethe user does not permanently omit herself from exposure. In anotherembodiment, the ads with said tags and related tags simply have theirweights reduced accordingly. Similar approaches to the above can beapplied to brand sorting.

System for Delivering Ads to Personas

FIG. 13 illustrates an exemplary system 1300 for creating personas andad serving to a persona on a computing device. As used herein, the term“ad” is to be interpreted broadly and can include promotional materials,rebates, consumer notices, content, political or religious materials,coupons, advertisements (including push advertisements), various kindsof recommendations (such as product/service recommendations,content/media recommendations), offers, content (movies/TV shows) andother information that a user may which to receive. At 1302 a mobiledevice is shown. On the screen are images representing four personastied to a single account. A user may optionally register the accountunder any identifier including an email address. In one embodiment, theemail address is one way hashed and discarded after the hash. The hashis optionally stored by the audience engine and serves as an identifier.This prevents the storage of user's identifying information on non-userdevices and enables the user to have an identifier in case she forgetsher password etc. In another embodiment, only one persona is created andno identifier is asked from the user. Instead, a software install ID orother identifier is used to identify the persona.

A persona may be created by optionally choosing a name for the persona,associated interests/keywords (e.g. to help focus ad searches), socialmedia accounts to tie the persona to and active locations/times thepersona should be active among other parameters. Then, a brand sortingscreen may be displayed at 1304. Upon sorting a number of brands, at1306 and 1308 the brands that define the persona are transmitted to anaudience engine 1310, which may be on a remote server.

The persona's demographic characteristics are matched with ads, offers,coupons, services, products, content recommendations or other similarthings. Typically, the audience engine 1310 is in communication with athird party ad server and/or ad bidding system (not shown). The ads maybe pre-downloaded to the audience engine 1310 and analyzed. Analysis maybe performed by assigning tags and associating statistical probabilitiesthat particular demographics would be interested in the ads or assigningprobabilities to existing tags or other data related to the ad. The adsare then optionally ordered in relevance to the characteristics of aparticular persona's vector as previously discussed. Here, in responseto the persona creation, a plurality of ads are pushed to the mobiledevice at 1312 from the audience engine 1310. The ads are pushed into alocal ad server 1316 on the user's computing device. Here the local adserver is within the application 1314 that created the persona. Withinthe application 1314, is an ad tracker 318 with a ticket book. Eachticket may be used to request an ad from an in-application persona API1322. In one embodiment, a ticket may contain information to display anad to one or more personas and/or to different devices or applicationsassociated with the persona.

The request for an ad may occur upon a user or a software request or onthe occurrence of an event such as an arrival of the device at aphysical location, keyword in communication, predetermined by anadvertiser, event on a calendar, time of a TV show, a triggering eventsuch as visiting a website, date of a product sale etc. API 1322 maystart the ad request at 1324, which is transmitted to ad tracker 1318.Ad tracker 1318 returns a return ad ticket at 1320 to API 1322. API 1322then submits the ad ticket and application ID at 1326 to the local adserver 316. The local ad server then displays the ad on the device orother connected devices at 1328. In one embodiment, the application IDat 1326 can be directed toward other applications on a plurality ofconnected devices in order for an ad to be shown on other devices.Optionally, upon display of the ad, at 1326 a request can be made to aconnected device to display other content such as a website related tothe displayed ad or the ad itself on other devices.

Masking User Identity

FIG. 14 illustrates a system 1400 in which a user's identity can beprotected from being discovered during persona advertising. In oneembodiment, a GUID or other non-traceable ID, such as a software installID, is assigned to each user/persona and this information is optionallyassociated with an IP address as the only information shared withadvertisers etc. At each exposure point, a new GUID may be assigned toprevent identity triangulation. In one embodiment, GUIDs areautomatically changed even on the same visit at every exposure point foradded privacy.

At the start operation, the in-app Advatar (persona) 1402 (typicallystored on the user's device within an application) has a Get_Ad 1404software module which requests a ticket (each ticket may contain adifferent GUID(s)) from an Advatar app 1406 on any desired deviceconnected to a network. The Advatar app may cache a plurality of ticketsin an ad ticket book 1408. The in-app Advatar 1402 is designed torequest/receive and display ads via tickets and optionally designed toaccept persona feedback on an ad and the persona's actions.

The ticket requested by the in-app Advatar 1402 is sent from the Advatarapp 1406 to the in-app Advatar 1402 with which the ticket is thenassociated with an application ID. The application ID is then sent to anadvertiser's ad server 1410, an ad exchange or real time bidding system.In one embodiment, different tickets may optionally correspond totickets to show different personas ads. From there, the ad ticket andappID is passed to a secure third party server (e.g., audience engine)1412 in which this sever, and optionally not the advertiser's server,knows what the ticket GUID means in terms of the user's identity orother sensitive information e.g., profile etc. Another use of the GUIDis that users may appear simultaneously as different GUIDs on differentdevices in a secure manner. For example, advertising server A would seethe GUID as 1234 and the same user is seen on advertising server B asuser GUID 4567 but only the server 1412 would be able to determine thetrue identity of the user. The apparent GUID may even changeperiodically while accessing the same website (server 1412 willperiodically assign a new GUID). The secure third party server 1412would coordinate the information with the correct master ID as only itknows the corresponding GUIDs and identity/persona information. Thisprotects the user from unwanted contact from advertisers such as SPAM asthe advertiser has no email or other personally identifiableinformation. Although in one embodiment, the ad server 1410 has theuser's IP address in order to return an appropriate ad to the persona.

Given the persona profile on the secure third party server 1412, anappropriate ad or kind of ad is determined. The appropriate type of adis then communicated to ad server 1410. The advertiser's server 1410then forwards the appropriate ad determined by the secure third partyserver 1412 to the in-app Advatar 1402 via an IP address that the in-appis hosted on. Once at Advatar 1402 a Show_Ad module 1414 then displaysor caches the ad for later display. Various other software embodimentsare contemplated for masking a user's identity.

Brand Sorting Embodiments

In the embodiment shown in FIG. 9, a plurality of brands are firstdisplayed in the neutral area 904 for sorting into the other areas or tobe left in area 904. Brands may be presented to a user based uponstatistical market research and the desired attributes to be collected.For instance, a “like” of the Huggies Diaper™ brand may suggest a highprobability distribution that one is a parent. Selection of Huggies andToys R' US™ brand may further confirm that one is a parent. Brands maybe suggested to a user based upon sites or actions that the user hasengaged in, installed apps, keywords or senders/recipients incommunications, geographic history (infers you have visited a locationrelated to a brand with a mobile device), contacts/friends, current orfuture locations, interests etc. Each of the brands may be weighted asdesired to help determine desired characteristics.

Upon brand sorting, ads and other recommendations can be displayed to auser. Upon ad feedback, the user may be displayed another series ofbrands (or ads) to vote on for a finer granularity of recommendations.In one embodiment, this ad voting may adjust values of a single personavector or even multiple personas. For instance, a demographic dimensionwithin the vector may be voted up or down by a desired amount dependingon how an ad is voted. For instance, if many ads that are targeted to acertain demographic are voted up, then that demographic dimension in thepersona may be adjusted up. However, to prevent a single dimensionwithin a persona vector from unduly influencing the entire personavector, dimensions can be optionally bounded.

In another embodiment of the brand sorter, different opinions can beasked depending on the desired context. The chart below illustrates someexamples:

Brands Advertising Offers Up Like More Like This Save Neutral Don't CareDon't Know Neutral Down Dislike Less Like This Discard

Different combinations and actions can be taken from the above chart.For instance, if a brand is “disliked” the brand's associated values maysimply be weighted down in the persona. However, if a brand is notliked, the brand's associated values may be completely discarded. Inaddition, any associated tags may be flagged as not suitable for theconsumer at all. Alternately, this “unsuitable” data may only bediscarded for a short time and gradually be reintroduced to the user.

In other embodiments additional information may be displayed to the userduring brand sorting during drag and drop selection. For example, as theicon 902 b in FIG. 9, is selected by a user with a finger and isgradually moved from its initial position, the initial position may beoccupied with “peek text” that serves as information in the spaceformerly occupied by the icon which may display additional informationsuch as the name of then brand in text etc.

Monetization Embodiments

FIG. 12 also illustrates a system for monetization of the personas. Hereaudience engine 1220 produces an audience of users whose personas fit adesired brand or advertiser definition such as coffee drinkers who livein Seattle and who are over 30 years old, which is gathered or inferredfrom brand sorting or other techniques.

The advertiser or brand 1240 can then use the resulting persona datafrom the audience engine 1220 to analyze their products, ad performance,marketing strategy against any desired audience. Product adeffectiveness to a persona(s) in desired audiences can be ascertained bycomparison of common and/or related tags between the persona and the adtags and associated tag values. Analysis could comprise analyzing uservotes on the ads, if the ad was clicked on by the user, if a product waspurchased etc. A fee could be charged for such services to theadvertiser 1240.

Email Accounts and Personas Embodiments

In one embodiment, under a single user account, each persona may beassociated with a separate email address. This permits the user to havean email address focused specifically on a single persona. Each personamy have the ability to decline/filter communications according tokeyword, sender, dates or other criteria to prevent the persona frombeing overwhelmed with unsolicited communications.

As illustrated in FIGS. 15-16, a persona may be associated with an emailprogram and an address to help organize information. New email addressesmay be created by appending information to existing email addresses. Forinstance, if an email is brian@roundtree.org, a new email address for apersona may be brian@roundtree.org.0mail.com or other methods can beused to create new email addresses.

The persona 1502 may access an email program as shown in FIG. 11. Theemail program may group persona emails by domain 1504 and may associatean icon and company name upon domain recognition. An active persona icon1502 may also be displayed.

An arbitrary level of importance assignment may be featured in whichhigh importance messages such as password assignments are given certainlevels that are marked next to the domain “level 1” indication andlesser important emails are given lesser importance levels.

FIG. 16 illustrates functionality of the email program for a specificpersona. Here, emails are listed by domain, assigned importance levelsand may be read. At 1602, advertising can be directed in the emailprogram using technology discussed in this document. Optionally, theadvertising may be based on the active persona and/or related to thesubject of the message being read. In addition, once the email is read,it is marked as viewed.

Description of Computer Hardware

Embodiments of the subject matter and the operations described in thisspecification can be implemented in digital electronic circuitry, or incomputer software, firmware, or hardware, including the structuresdisclosed in this specification and their structural equivalents, or incombinations of one or more of them. Embodiments of the subject matterdescribed in this specification can be implemented as one or morecomputer programs, i.e., one or more modules of computer programinstructions, encoded on computer storage medium for execution by, or tocontrol the operation of, data processing apparatus.

A non-transitory, computer storage medium can be, or can be included in,a computer-readable storage device, a computer-readable storagesubstrate, a random or serial access memory array or device, or acombination of one or more of them. Moreover, while a computer storagemedium is not a propagated signal, a computer storage medium can be asource or destination of computer program instructions encoded in anartificially-generated propagated signal. The computer storage mediumalso can be, or can be included in, one or more separate physicalcomponents or media (e.g., multiple CDs, disks, or other storagedevices). The operations described in this specification can beimplemented as operations performed by a data processing device usingdata stored on one or more computer-readable storage devices or receivedfrom other sources. A representative data processing device is shown inFIG. 17.

The data processing device includes “processor electronics” thatencompasses all kinds of apparatus, devices, and machines for processingdata, including by way of example a programmable microprocessor 1702, acomputer, a system on a chip, or multiple ones, or combinations, of theforegoing. The apparatus can include special purpose logic circuitry,e.g., an FPGA (field programmable gate array) or an ASIC(application-specific integrated circuit). The apparatus also caninclude, in addition to hardware, code that creates an executionenvironment for the computer program in question, e.g., code thatconstitutes processor firmware, a protocol stack, a database managementsystem, an operating system, a cross-platform runtime environment, avirtual machine, or a combination of one or more of them. The apparatusand execution environment can realize various different computing modelinfrastructures, such as web services, distributed computing and gridcomputing infrastructures.

A computer program (also known as a program, software, softwareapplication, script, or code) can be written in any form of programminglanguage, including compiled or interpreted languages, declarative orprocedural languages, and it can be deployed in any form, including as astand-alone program or as a module, component, subroutine, object, orother unit suitable for use in a computing environment. A computerprogram may, but need not, correspond to a file in a file system. Aprogram can be stored in a portion of a file that holds other programsor data (e.g., one or more scripts stored in a markup languagedocument), in a single file dedicated to the program in question, or inmultiple coordinated files (e.g., files that store one or more modules,sub-programs, or portions of code). A computer program can be deployedto be executed on one computer or on multiple computers that are locatedat one site or distributed across multiple sites and interconnected by acommunication network.

The processes and logic flows described in this specification can beperformed by one or more programmable processors executing one or morecomputer programs to perform actions by operating on input data andgenerating output. The processes and logic flows can also be performedby, and apparatus can also be implemented as, special purpose logiccircuitry, e.g., an FPGA (field programmable gate array) or an ASIC(application-specific integrated circuit).

Processors suitable for the execution of a computer program include, byway of example, both general and special purpose microprocessors, andany one or more processors of any kind of digital computer. Generally, aprocessor will receive instructions and data from a read-only memory ora random access memory or both. The essential elements of a computer area processor for performing actions in accordance with instructions andone or more memory devices for storing instructions and data. Generally,a computer will also include, or be operatively coupled to receive datafrom or transfer data to, or both, one or more mass storage devices 1704for storing data, e.g., flash memory, magnetic disks, magneto-opticaldisks, or optical disks. However, a computer need not have such devices.Moreover, a computing device can be embedded in another device, e.g., amobile telephone (“smart phone”), a personal digital assistant (PDA), amobile audio or video player, a handheld or fixed game console (e.g.Xbox 360), a Global Positioning System (GPS) receiver, or a portablestorage device (e.g., a universal serial bus (USB) flash drive), to namejust a few. Devices suitable for storing computer program instructionsand data include all forms of volatile or non-volatile memory, media andmemory devices, including by way of example semiconductor memorydevices, e.g., EPROM, EEPROM, and flash memory devices; magnetic disks,e.g., internal hard disks or removable disks; magneto-optical disks; andCD-ROM and DVD-ROM disks. The processor and the memory can besupplemented by, or incorporated in, special purpose logic circuitry.

To provide for interaction with a user, embodiments of the subjectmatter described in this specification can be implemented on a computerhaving a display device 1308, e.g., an LCD (liquid crystal display), LED(light emitting diode), or OLED (organic light emitting diode) monitor,for displaying information to the user and an input device 1706 such asa keyboard and a pointing device, e.g., a mouse or a trackball, trackpad, temperature sensor, accelerometer, light sensor, audio sensor,wireless signal detection sensor etc., by which the user can provideinput to the computer. In some implementations, a touch screen can beused to display information and to receive input from a user. Otherkinds of devices can be used to provide for interaction with a user aswell; for example, feedback provided to the user can be any form ofsensory feedback, e.g., visual feedback, auditory feedback, or tactilefeedback; and input from the user can be received in any form, includingacoustic, speech, or tactile input. In addition, a computer can interactwith a user by sending documents to and receiving documents from adevice that is used by the user; for example, by sending web pages to aweb browser on a user's client device in response to requests receivedfrom the web browser. The data processing apparatus 1700 may alsoinclude a wireless transceiver 1712 such a cellular radio, WiFi or WiMaxtransceiver, Bluetooth transceiver and a network connection 1714 etc.The data processing device may also include an output device such as aprinter 1710. In addition, the device may include location sensingdevices (GPS etc.), as well as clocks and other circuitry (not shown).

As shown in FIG. 14, embodiments of the subject matter described in thisspecification can be implemented in a computing system 1800 thatincludes a back-end component, e.g., as a data server 1850, or thatincludes a middleware component, e.g., an application server, or thatincludes a front-end component, e.g., a client computer 1700 having agraphical user interface or a Web browser 1890 a through which a usercan interact with an implementation of the subject matter described inthis specification, or any combination of one or more such back-end,middleware, or front-end components. The components of the system can beinterconnected by any form or medium of digital data communication,e.g., a communication network. Examples of communication networksinclude a wired or wireless local area network (“LAN”) and a wide areanetwork (“WAN”), an inter-network 1810 (e.g., the Internet), andpeer-to-peer networks (e.g., ad hoc peer-to-peer networks).

The computing system can include any number of clients and servers. Aclient and server are generally remote from each other and typicallyinteract through a communication network. The relationship of client andserver arises by virtue of computer programs running on the respectivecomputers and having a client-server relationship to each other. In someembodiments, a server 1850 transmits data (e.g., an HTML page) to aclient device 1700 (e.g., for purposes of displaying data to andreceiving user input from a user interacting with the client device).Data generated at the client device (e.g., a result of the userinteraction) can be received from the client device at the server. Inthe embodiment shown in FIG. 17, the server computer 1850 operatesserver engine software 1860 and web management software 1870 to receivedata from and send data to remote clients. In addition, the servercomputer operates a database 1890 b to store persona information forusers who wish to receive ads as described above. Content managementsoftware 1880 and database management software 1890 allow the servercomputer to store and retrieve persona information from the database andto search the database for personas that meet advertiser's criteria fora target audience.

From the foregoing, it will be appreciated that specific embodiments ofthe invention have been described herein for purposes of illustration,but that various modifications may be made without deviating from thespirit and scope of the invention. Accordingly, the invention is notlimited except as by the appended claims.

I claim:
 1. A processor-based system, comprising: memory for storinginstructions that are executable by processor electronics; the processorelectronics configured to execute the instructions in order to:configure a first private partition in the memory to store customer datathat is accessible by a first user and not by a second user; configure asecond private partition in the memory to store customer data that isaccessible by the second user and not by the first user; create a firstgeneric data partition by analyzing the customer data in the firstpartition and removing identifying data by: determining identifyingcustomer data by cross referencing data in at least a portion of thefirst private partition with non-generic marketing data; andsubstituting generic marketing data with substantially similarassociated probabilities for the identifying customer data; and whereinthe first generic data partition is configured to exclude second useraccess.
 2. The system of claim 1, wherein the memory stores instructionsthat further cause the processor electronics to: determine if therewould be an increase in the detail of the customer data in the firstprivate partition if customer data in the first private partition wereintegrated with at least a portion of the customer data in the secondpartition; and in response to a determination that there would be anincrease in the detail of customer data in the first private partition,configure the first generic data partition to be accessible by thesecond user in exchange for the integration of at least some of thecustomer data in the second partition with the customer data in thefirst partition.
 3. The system of claim 2, wherein the memory storesinstructions that cause the processor electronics to determine thatthere is an increase in the customer detail in the first privatepartition if the portion of customer data in the second partitionincludes: additional data that is similar in value to that of thecustomer data in the first private partition; or data based on a largernumber of customers in comparison to the number of customers representedby the customer data in the first private partition.
 4. The system ofclaim 2, wherein the memory stores instructions that cause the processorelectronics to determine that there is an increase in the customerdetail in the first private partition if the portion of the customerdata in the second partition includes: unrelated customer data to thatin the first private partition.
 5. The system of claim 2, wherein thememory stores intructions that cause the processor electronics todetermine that there is an increase in the customer detail in the firstprivate partition if the portion of the customer data in the secondpartition includes: a different data category than those in the firstprivate partition; or at least one category with a larger amount of datain comparison to the amount of data of the same category in the firstprivate partition.
 6. The system of claim 2, wherein the memory storesinstructions that cause the processor electronics to calculate a newdata partition based on the first generic data partition and the secondprivate partition.
 7. The system of claim 1, wherein the memory storesinstructions that cause the processor electronics to create arelationship link between data associated with a tag in the firstprivate partition and data associated with a similar tag in the secondprivate partition.
 8. The system of claim 1, wherein the memory storesinstructions that cause the processor electronics to create arelationship link between data associated with a tag in the firstprivate partition and data associated with a similar tag in the firstgeneric data partition.
 9. The system of claim , wherein the customerdata in the first private partition is comprised of: a usercharacteristic and a statistical probability associated with said usercharacteristic, said user characteristic and statistical probabilitybased at least upon a user content affinity input.
 10. The system ofclaim 1, wherein the customer data in the first private partition iscomprised of: a user characteristic and a statistical probabilityassociated with said user characteristic.
 11. A non-transitory computerreadable medium having instructions stored thereon that are executableby processor electronics in order to determine generic customer data by:finding customer data in a first data partition and the customer data inanother data partition that are in common between the partitions,wherein the common customer data are associated with probabilities;combining the associated probabilities between common customer data fromthe partitions; and selecting the customer data, that are in both thefirst partition and the other data partition and any associated combinedprobabilities as the generic customer data.
 12. The non-transitorycomputer readable medium of claim 11, wherein the instructions includeinstructions to further cause the processor electronics to create ageneric data partition comprised of the generic customer data.
 13. Thenon-transitory computer readable medium of claim 12, wherein theinstructions include instructions to further cause the processorelectronics to create a relationship link between customer dataassociated with a tag in the first data partition and data associatedwith a similar tag in the generic data partition.
 14. The non-transitorycomputer readable medium of claim 11, wherein the customer data in thefirst data partition is comprised of: a user characteristic and astatistical probability associated with said user characteristic, saiduser characteristic and statistical probability based at least upon auser input.
 15. The non-transitory computer readable medium of claim 11,wherein the customer data in the first data partition is comprised of: auser characteristic and a statistical probability associated with saiduser characteristic.
 16. A non-transitory computer readable mediumhaving instructions stored thereon that are executable by processorelectronics in order to: upload customer data comprised of tags andassociated statistical probabilities to a private partition on a remotedevice; instruct the remote device to configure the private partition toprevent access to customer data in the partition; and instruct theremote device to allow access to the customer data in the privatepartition by a user in response to the user offering offer data if theoffer data is comprised of: generic customer data from which identifyingdata has been removed.
 17. The non-transitory computer readable mediumof claim 16, wherein the customer data in the private partitionincludes: a tag representing an end user characteristic; and astatistical probability associated with said end user characteristic,said end user characteristic and statistical probability based at leastupon user input.
 18. The non-transitory computer readable medium ofclaim 16, wherein the customer data in the private partition includes: atag representing an end user characteristic; and a statisticalprobability associated with said end user characteristic.
 19. Thenon-transitory computer readable medium of claim 16, wherein thecustomer data in the private partition further includes data based asleast on customer communication messages.